The neural network, as is generally and conventionally known, is comprised of neurons in parallel layers. According to each neuron, output data 0 are output corresponding to the comparison results between the sum of multiplied input data I1, I2, I3 . . . In by weights W1, W2, W3 . . . Wn and threshold .theta.. Various comparison methods are possible. For example, as to normalization function 1 [f], output data 0 can be expressed as: EQU O=1 [.SIGMA.W.sub.n .multidot.I.sub.n -.theta.]
Here, when .SIGMA.W.sub.n .multidot.I.sub.n exceeds a threshold .theta., output data becomes "1", and when .SIGMA.W.sub.n .multidot.I.sub.n is less than threshold .theta., it becomes "0".
As mentioned above, neurons will output a "1" or "0" in response to input data. The ignition pattern of the above neurons depends on input data.
Conventionally, a method for the recognition of characters by a neural network is disclosed in a patent publication hei 1-290582 in which characteristics data of characters (for example, number of end points, number of branch points, etc.) are calculated so as to judge which character the above characteristics data corresponds to.
In addition to the above character recognition, recognition accuracy is an important factor for the above data processing system, which is based on how accurately an input data consisting of a plurality of characteristics data can be recognized. Also, efficiency is essential for a practical system on how the above recognition function is realized with minimum neural network. According to the above conventional example, the effective characteristics data are used for the printed Japanese characters. However, it is unknown what characteristics data may be effectively used for hand writing characters, running style writing characters, European hand writings, special characters such as Arabic, etc. The more characteristics data are applied, the bigger the scale of data processing system becomes.